System for the Assessment of Sleep Quality in Adults and Children

ABSTRACT

Systems and methods for assessment of sleep quality in adults and children are provided. These techniques include an apparatus worn above the forehead containing the circuitry for collecting and storing physiological signals. The apparatus integrates with a sensor strip and a nasal mask to obtain the physiological signals for the user. The form factor of this apparatus is comfortable, easy to self-apply, and results in less data artifacts than conventional techniques for capturing physiological data for analyzing sleep quality. Neuro-respiratory signals are analyzed using means to extract more accurate definitions of the frequency and severity of sleep discontinuity, sleep disordered breathing and patterns of sleep architecture. Biological biomarkers and questionnaire responses can also be compared to a database of healthy and chronically diseased patients to provide a more accurate differential diagnosis and to help determine the appropriate disease management recommendations.

RELATED APPLICATION

This application claims the benefit of U.S. provisional patentapplication Ser. No. 61/160,924 entitled “SYSTEM FOR THE ASSESSMENT OFSLEEP QUALITY IN ADULTS AND CHILDREN,” filed on Mar. 17, 2009, which isincorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of monitoring sleeparchitecture and more specifically to systems and methods for assessingthe sleep quality of adults and children.

BACKGROUND

Sleep is essential for survival and poor sleep quality is a principalcontributor to chronic diseases. Typically an individual has four to sixsleep cycles per night, each between 60 and 120 minutes in length andcomprising of different proportions of rapid eye movement (REM) sleepand non-REM sleep (that is further divided into stages N1, N3 and N3).Each sleep cycle typically begins with non-REM sleep and ends with REMsleep. The first half of the night contains most of the N3 or slow wavesleep (SWS), whereas rapid eye movement (REM) sleep is most prominent inthe second half. SWS is considered the deepest and most restorative ofsleep stages during which there is a reduction in heart rate, bloodpressure, sympathetic nervous activity, and brain glucose metabolism,and increase in vagal tone. Hypothalamo-pituitary—adrenal activity issuppressed during SWS and increased during REM sleep. The sequence ofsleep stages (NREM sleep stages 1, 2, 3 or the REM sleep stage) duringan (overnight) sleep or (daytime) nap, sometimes interrupted with briefperiods of wakefulness, is referred to as sleep architecture.

Poor sleep quality resulting from sleep fragmentation or abnormal sleeparchitecture alters the proportion of REM, non-REM and SWS obtained pernight. When a normal individual is chronically sleep deprived, the onsetof REM in the first sleep cycle is faster and the total amount of REMand SWS changes. The amount of lighter stages of NREM sleep (stage N1)is decreased. In contrast to this sleep deprivation in a healthyindividual, there are a number of medical conditions that contribute torepetitive arousals during the night. This leads to sleep fragmentationand a different sort of chronic inadequate sleep. The frequency of thearousals and their impact on sleep disruption is dependent both on thesleep stage when the arousal occurs and one's susceptibility to sleepdisturbances. Individuals are least likely to have arousals or to awakenfrom an arousal during SWS and most likely during stage N1 NREM and REMsleep. Frequent arousals can disrupt sleep architecture with patterns ofawakening that limit the amount of REM and slow wave sleep. Chronicinadequate sleep also manifests in patients who suffer from an inabilityto easily fall asleep or maintain sleep throughout the night. Theassessment of sleep architecture is useful to physicians trying todetermine the appropriate diagnosis. For example, in major depression,patients exhibit increased sleep latency, frequent arousals, difficultyremaining asleep, and decreased in slow wave sleep. The first episode ofREM sleep will appear earlier than usual, with an increase in totalpercentage of REM sleep, longer duration REM sleep periods, andincreased eye movement density (referred to as REM sleep disinhibition).Patients with post-traumatic stress syndrome can also exhibit abnormalsleep architecture i.e., an increase the amount of REM sleep per night,similar to depressed patients.

Neurological signals i.e., electroencephalograph (EEG) and/orelectrooculargram (EOG) are extremely sensitive to the measurement ofsleep stage/sleep architecture and sleep quality but fairly insensitivein the assessment of sleep disordered breathing. The detection ofarousals during sleep can be measured by multiple means includingcortical (i.e., EEG, sympathetic (e.g., electrocardiograph (ECG), pulserate or peripheral arterial tone), or behavioral (e.g., changes inrespiration, movement or position, etc.) approaches. Cortical arousalsmeasured by EEG is the gold-standard measure of sleepdisturbance/fragmentation although the reliability of visual scoredevents may be less sensitive than beat-to-beat changes in cardiacfunction. The frequency and duration of the arousal, its temporalassociation with breathing, and the position of the head when thearousal occurs all provide information useful in differentiating theunderlying medical condition. For menopausal related sympatheticarousals, the gradual increase in core body temperature that results ina hot flash precedes the arousal during non-REM sleep while the hotflash follows the awakening during REM sleep. The intensity and timingof the hot flash can impact the ability to return to sleep andcontributes to sleep maintenance insomnia.

Respiratory effort related arousals are triggered by a full or partialcollapse of the upper airway (i.e., sleep disordered breathing orObstructive Sleep Apnea (OSA)) as a response to return airway patency.The contribution of OSA to poor sleep quality increases the risk ofaccidents due to daytime drowsiness and has been associated withhypertension, increased risk of congestive heart failure, coronaryartery disease, myocardial infarction, cardiac arrhythmias, asthma,diabetes and stroke.

Abnormal subcortical motor system activation associated with periodiclimb movements or restless leg syndrome in adults and children canresult in arousal sequences similar to OSA that compromise sleep qualityand cause sleep deprivation. Environmental conditions such as noisei.e., snoring bed partner, passing vehicles, etc. or sleeping away fromhome can contribute to delayed sleep onset, abnormal sleep architecture,and increased susceptibility to arousals.

Conventional focused respiratory- or cardio-based approaches are highlysensitive to breathing-related sleep disordered such as OSA butrelatively insensitive to the assessment of sleep architecture or sleepquality. In conventional techniques, respiration is measured by multiplemeans, including airflow, respiratory effort, and/or ECG-relatedchanges. While the most accurate and direct method of monitoringrespiratory effort is by measurement of changes in intrathoracicpressure by use of an esophageal catheter, this procedure is invasiveand not well tolerated by the patient. Respiratory effort is mostcommonly measured with bands placed around the chest and abdomen toassess breathing-related change in compartmental circumference. Pulsetransit time (PTT) measures the time it takes for a pulse pressure waveto travel from the aortic valve to the periphery. Theelectrocardiographic (ECG) r-wave is used as the start-time, and thearrival of the pulse at the finger reflects the blood pressurefluctuations induced by negative pleural pressure swings. Diaphragmaticelectromyography (EMG) provides a fourth measurement of inspiratoryeffort and has been shown to have a good correlation with increases inesophageal pressure. Changes in forehead venous pressure represent a newapproach that has been shown to measure changes in respiratory effort.

The measurement of airflow is routinely performed by affixing a pressuretransducer to a nasal cannula positioned in the nostrils. When a cannulais used measure nasal pressure, the portion inside the nose act as aresistor, and the pressure drop between the nasal cavity and theatmosphere acts like a crude pneumotachometer. The positioning anddisplacement of the cannula tips inside the nostrils, the size of thenasal openings, and whether the subject is mouth breathing impact theamplitude and shape of the nasal pressure signal. Amplitude variabilityis less problematic when visual scoring is employed, however amplitudechanges can be problematic with mathematically-based measures of tidalvolume changes. An advantage of nasal pressure airflow (vs. thermistoror sound-based flow measures) is that shape of airflow signal can beevaluated to assess flow limitation.

Chronically poor sleep quality and associated reduction in SWS resultsin decreased insulin sensitivity, reduced glucose tolerance andincreased risk of type 2 diabetes. Poor sleep quality resulting frommenopausal hot flashes is associated with insomnia, depression, anxiety,mood disorders, and cognitive and memory impairment. Low sleepefficiency, abbreviated total sleep time and shortened REM sleep timescontributes to the severity of drug-resist hypertension. Sleepdeprivation causes an imbalance between leptin and ghrelin (opposingmetabolic counterparts which control hunger and food intake) and leadsto increased consumption of high carbohydrate foods, weight gain, andobesity. Poor sleep quality unrelated to sleep disordered breathing hasbeen associated with chronic, low-grade inflammation in otherwisehealthy young women which increases the risk of future adverse healthoutcomes. Increased inflammatory markers were found to be sleep durationdependant in women, but not men. Poor sleep quality has explainedelevated levels of Interleukin-6 (IL-6), a pro-inflammatory cytokineassociated with sleep apnea, narcolepsy, insomnia, excessive daytimedrowsiness and fatigue. Elevated IL-6 was strongly associated withdecreased sleep efficiency, increased REM latency, and percentage ofwaking incidences after sleep onset. Other inflammatory markers, such asC-reactive protein are also associated with similar sleep disturbancesassociated with sleep apnea, and interactions of sleep disturbances andC-reactive protein are associated with cardiovascular diseases. Elevatedinflammation is associated with many diseases, including cancer,cardiovascular disease, hypertension, chronic fatigue syndrome,fibromyalgia, depression, and autoimmune disorders among others.Elevated inflammatory cytokines (and other inflammation markers, such asC-reactive protein) are associated with earlier onset of disability inthe elderly, increased risk of cardiovascular disease and hypertension,as well as increased risk of diabetes and metabolic X syndrome. In thesedisease states, elevated inflammation interacts with sleep disturbancesin a bi-directional manner to exacerbate the disease state.

It is not uncommon for patients to be misdiagnosed due to overlappingco-morbid symptomology (e.g., untreated OSA to be misdiagnosed asinsomnia). Older women are particularly susceptible to misdiagnosisbecause of the increased risk of OSA as progesterone levels decline andthe assumption that menopausal related symptoms are the source of thedepressed state or reported problem with sleep. Conversely, patients maybe misdiagnosed with OSA when the sleep fragmentation related to thenumber of cortical or sympathetic arousals is disproportionately greaterthan a mild case of obstructive breathing event. The overlappingbehavioral symptomology of childhood OSA, limb movements during sleep,and Attention Deficit/Hyperactivity Disorder (ADHD) provide otherexamples where of the difficulty of a differential diagnoses,particularly for a pediatrician with only general knowledge regardingthese disorders. Accurate characterization of the etiology of poor sleepquality requires measurement of signals that reflect respiratory,cardiological, and neurological physiology. Thus, it would be useful tosimply and easily acquire all sets of measures and combine thisinformation with medical history information and a database of responsesto assign pre-test probabilities and/or assist clinicians inconstructing their differential diagnoses.

SUMMARY

Systems and methods for assessment of sleep quality in adults andchildren are provided. The assessment of sleep quality includesperforming concurrent measurements of two categories of signal data: (1)signal data related to sleep states, and (2) signal data related to atype of sleep disruption (also referred to herein as “arousals”). Thetype of sleep disruption can be used to help diagnose what may becausing poor sleep quality in a patient. These techniques include anapparatus worn above the forehead containing the circuitry forcollecting and storing physiological signals. The apparatus can beintegrated with or connected to a sensor strip and can also integratewith or be connected to a nasal mask to obtain the physiological signalsfor the user. The form factor of this apparatus is comfortable, easy toself-apply, and results in less data artifacts than conventionaltechniques for capturing physiological data for analyzing sleep quality.Neuro-cardio-respiratory signals are analyzed using means to extractmore accurate definitions of the frequency and severity of sleepdiscontinuity, sleep disordered breathing and patterns of sleeparchitecture. Biological biomarkers and questionnaire responses can alsobe compared to a database of healthy and chronically diseased patientsto provide a more accurate differential diagnosis and to help determinethe appropriate disease management recommendations.

Neuro-respiratory signals are analyzed to extract more accuratedefinitions of the frequency and severity of sleep discontinuity, sleepdisordered breathing and patterns of sleep architecture.

According to an embodiment, a system for assessing sleep quality of auser is provided. The system includes a sensor strip comprising at leastone sensor configured to detect data indicative of sleep disturbances ofa user. The sensor strip is positioned on the user's forehead. Thesystem also includes a wearable data acquisition unit coupled to thesensor strip. The data acquisition unit is configured to receive andcollect data from the sensors in the sensor strip.

According to another embodiment, a system for assessing sleep quality ofa user is provided. The system includes a wearable data acquisition unitfor concurrently measuring data indicative of a sleep state of a userand indicative of sleep disturbances experienced by a user. The systemalso includes a sensor strip coupled to the data acquisition unit. Thesensor strip includes at least one sensor configured to detect apsychophysiological data of the user and to provide the data to the dataacquisition unit. The data acquisition unit is configured to receivedata from the sensors in the sensor strip.

According to yet another embodiment, a system for assessing sleepquality of a user is provide. The system includes a sensor strip thatincludes at least two sensors sensors configured to detect dataindicative of sleep disturbances of a user, and a wearable dataacquisition unit coupled to the sensor strip. The at least two sensorsare configured to collect data of a different type, and the sensor stripis removably affixed to the user's body. The data acquisition unit isconfigured to receive and collect data from the sensors in the sensorstrip.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of the method for assessing sleep qualityaccording to an embodiment;

FIGS. 2A and 2B illustrate a patient with the data acquisition systemincluding the data acquisition unit, sensor strip, and nasal mask andnasal cannula according to two embodiments;

FIG. 3 is a block diagram identifying functional components and circuitsof a data acquisition apparatus for quantifying sleep quality accordingto an embodiment;

FIG. 4 is a block diagram of integrated wireless sensors affixed todifferent locations on the body for acquiring the physiological signalsaccording to an embodiment;

FIG. 5 is an illustration of the integrated sensor strip of FIGS. 2A and2B used to acquire physiological signals according to an embodiment;

FIGS. 6A and 6B illustrate the data acquisition unit's enclosure andinterface with the sensor strip according to an embodiment;

FIGS. 7A, 7B, and 7C illustrate views of a disposable nasal mask capableof obtaining minute changes in airflow according to an embodiment;

FIG. 8 includes example data illustrating changes in patterns ofphysiological signals which define behavioral measures of sleep/wake andthe impact of position on sleep disordered breathing patterns as aresult of position change according to an embodiment.

FIG. 9 includes example data illustrating how actigraphic measures ofmovement can be confirmed with snoring patterns to recognize behavioralsleep/wake patterns according to an embodiment.

FIG. 10 includes example data illustrating how the capability ofactigraphy to measure increased breathing effort associated with loudsnoring according to an embodiment.

FIG. 11 includes example data comparing respiration information providedwith actigraphy compared to conventional measures of respiratory effortaccording to an embodiment;

FIG. 12 includes example data that presents patterns associated withperiodic limb movements that could be detected with actigraphy; and

FIG. 13 illustrates an exemplary computer system that can be used inconjunction the DAU according to an embodiment.

DETAILED DESCRIPTION

Systems and methods for the assessment of sleep quality in adults andchildren are provided. The assessment of sleep quality includesperforming concurrent measurements of two categories of signal data: (1)signal data related to sleep states, and (2) signal data related to atype of sleep disruption (also referred to herein as “arousals”). Thesetechniques system allow clinicians to more accurately relate sleepquality with the underlying disease/disorder. Given the substantialnight to night variability in physiology, the patient's home is thepreferred environment for acquiring multiple night studies. Thus,embodiments use electrodes and sensors that can be self-applied withlimited skin or scalp preparation, and which monitors signal qualityduring use and provides user feedback when signal quality problems aredetected.

The techniques disclosed herein provide a benefit over conventionaltechniques for monitoring sleep quality, by concurrently collecting datarelated to sleep architecture and sleep disruption using a compact andeasy to use system that can be employed in a user's home. Conventionaltechniques require that patients be monitored at a sleep laboratory,where patients often have trouble sleeping well. Some conventionaltechniques can be used by patients in the home, but they are harder forthe patient to use and do not allow for concurrent collection of datarelated to sleep architecture and sleep disruption.

FIG. 1 is a flow diagram of the method for assessing sleep qualityaccording to an embodiment. The method can be used to assess theseverity and impact of poor sleep quality. FIG. 1 will be discussed indetail below after introducing various embodiments of systems that canbe used for implementing this method.

FIGS. 2A and 2B illustrate a patient with the data acquisition systemincluding the data acquisition unit, sensor strip, and nasal mask andnasal cannula according to two embodiments. The data acquisition systemcan be used to collect and store physiological signals from a user whilethe user is sleeping in order to assess sleep quality. According to anembodiment, the data acquisition system can connect to an externalcomputer system that is configured to process the data collected by thedata acquisition system (see FIG. 3 described below). In someembodiments, the data acquisition system can be configured to perform atleast some analysis on the data collected before the data is downloadedto the external computer system.

Data Acquisition Unit (DAU) 210 is worn above the forehead of the userduring sleep to collect physiological signal data. In the embodimentillustrated in FIG. 2A, the DAU 210 is integrated or coupled with asensor strip 220 and a nasal pneumotachometer 240. FIG. 5, describedbelow, illustrates an embodiment of the sensor strip 220, and FIGS. 7A,7B, and 7C illustrate several views of an embodiment of nasalpneumotachometer mask 240. In the embodiment illustrated in FIG. 2B, analternative arrangement that includes a nasal cannula 250 is provided. Aheadband 230 encircles the rear of the patient's head to hold the dataacquisition system in place. A top strap 235 also extends over the backof the patient's head where it joins the headband 230 for additionalstability. Sensor strip 220 can be coupled to headband 230 to hold thesensor strip 220 in place over the user's forehead.

According to an embodiment, the headband 230 and/or the top strap 235can be adjusted in size to accommodate users having different sizedheads. In some embodiments, can be removed and replaced with differentsized headbands and top straps to accommodate different users.Furthermore, the headband and top straps can be designed to beone-time-use components for sanitary purposes that can be removed whileallowing the data acquisition unit and/or other components of theapparatus to be used by another user.

In an embodiment, the DAU 210 includes physiological acquisition andstorage circuitry configured to assess sleep quality or record data foruse in assessing sleep quality. As described above, the assessment ofsleep quality includes performing concurrent measurements of twocategories of signal data: (1) signal data related to sleep states, and(2) signal data related to the type of sleep disruption. DAU 210 isconfigured to perform the concurrent measurements of the sleep data,record these measurements, and in some embodiments, analyze and processthe recorded data. The DAU 210, sensor strip 220, and headband 230 canbe used to implement the method illustrated in FIG. 1. According to someembodiments, DAU 210 can be positioned near the top of the head of theuser (as illustrated in FIG. 2) or positioned over the forehead of theuser. The position of the DAU 210 can be based in part on the type ofassessment to be performed and the types of sensor data used to makethat type of assessment.

According to an embodiment, sensor strip 220 is removable, and in someembodiments, sensor strip 220 can also be disposable. For example, thesensor strip 220 can be configured to be electronically coupled to theDAU 210 using a socket connection or other type of connection thatallows the sensor strip 220 to be removed and replaced. This can allowthe sensor strip to be replaced for sanitary purposes (as well as thetop strap and/or the headband, as described above) to allow the DAU 210to be used again with another user. In an embodiment, the sensor strip220 can be a one-time-use strip that is provided in a sealed sterilepackage. In some embodiments, elements of sensor strip 220 can bedisposable, while some components are reusable. For example, the sensorstrip 220 may include disposable EEG sensors and a reusable thepulse/oximetry sensor. One skilled

According to an embodiment, sensor strip 220 can also include anadhesive backing that helps to facilitate and maintain placement of thesensor strip 220 on the user's forehead region by removeably adhering tothe user's skin. In one embodiment, the sensor strip 220 can compriseadhesive backed foam. The adhesive backing can also help to maintainsensor contact with the user's skin for those sensors that require skincontact. According to some embodiments, conductive sensors included inthe sensor strip 220 can have a conductive gel placed over thesessensors. FIG. 5, described below, illustrates some example of the typesof sensors than be included in the sensor strip 220. The configurationof the sensor strip 220 facilitates use of the data acquisition systemat home by users by making proper placement and attachment of sensorsmuch easier than conventional systems. For example, some conventionalEEG monitoring systems require that numerous electrodes be affixed to apatient's head. Proper placement of the electrodes is important. As aresult, EEG data is often gathered in a clinical setting where theelectrodes can be affixed to the patient by a clinician. When performingsleep studies, this can have a negative impact on the results of thestudy, because the user is removed from his or her normal sleepingenvironment and placed into an unfamiliar clinical setting. The sensorstrip 220 used with the data acquisition systems disclosed hereinfacilitates home use of the device by making proper placement of thesensors easy for patients, thereby allowing users to gather data at homewhere they are likely to be more comfortable and more likely toexperience sleep episodes that are more typically of their regular sleepepisodes.

FIG. 3 is a block diagram identifying functional components and circuitsof a data acquisition apparatus for quantifying sleep quality accordingto an embodiment. As described above, the assessment of sleep qualityincludes performing concurrent measurements of two categories of signaldata: (1) signal data related to sleep states, and (2) signal datarelated to the type of sleep disruption. The DAU 210 includes ananalog-to-digital converter 312 for amplifying and digitizing twochannels of EEG/EOG data 310 for measuring sleep architecture andcortical arousals, and one channel of ECG data 311 to assess heart rateand autonomic/cortical arousals. According to other embodiments, anycombination of EEG channels could be employed. However, a single channelof EEG can reduce the accuracy of the sleep stage measurement and morethan two channels can increase the size of the DAU without significantlyincreasing detection accuracy. The use of two channels can significantlyincrease the system's ability to differentiate REM from NREM sleep onthe basis of rapid conjugate eye movements that are characteristic ofREM sleep and appear as large voltage deflections that are out of phasein the two EEG channels. According to an embodiment, the EEG/EOG data310 and EEG data can be captures using electrodes integrated into sensorstrip 220. FIG. 5, which is described in detail below, provides anexample embodiment of one possible configuration of the sensor strip 220that includes EEG/EOG/EMG electrodes for gathering the EEG/EOG data 310and the ECG data 311.

DAU 210 is configured to receive a signal from a pressure transducer 313to acquire airflow data. The airflow data can be used in identifyingsleep disruptions, such as apnea. In a preferred embodiment the dynamicrange of the pressure transducer is set to optimize airflow resolutionof (i.e., +/−2 cm/H20).

Acoustic microphone 314 can also be used to detect snoring and/or otheraudible symptoms that can be causing sleep disruption. DAU 210 includesan amplification circuit that receives and amplifies sound signals fromacoustic microphone 314. In some embodiments, the acoustic microphone314 can be integrated into the DAU 314, while in other embodiments, theacoustic microphone 314 can be included in the sensor strip 220 oraffixed to the headband 230. In a preferred embodiment, a high fidelitysound is sampled between 2 to 4 kilohertz to profile snoring pattern andto recognize the region of airway obstruction as well as assessnocturnal coughing and wheezing. Alternatively, in some embodiments,snoring sounds can be quantified by rectification, integration, andsampling at a reduced frequency (e.g., 10 Hz) or with sensors limited toqualitative measures (e.g., vibration).

The DAU 310 includes an accelerometer 317 that can measure a full rangeof head positions, including both sleep and wake conditions, as well asbehavioral arousals defined by subtle head movements.

In the embodiment illustrated in FIG. 3, the DAU 210 includes a batterypower component 318 that includes a rechargeable lithium polymer battery320 and a power supply and recharging circuitry 319 for receiving powerfrom an external source for recharging battery 320 and/or powering theDAU 210. The battery power component 318 allows the DAU 210 to operatewithout requiring the DAU 210 to be tethered to an external power cordor power supply, which could be inconvenient and uncomfortable for auser of the device. According to some embodiments, an external powersupply can be used to power the device. According to other embodiments,battery 320 can be another type of battery 320 and in some embodimentsbattery 320 can be removable and replaceable.

A sensor driving unit 323 is included to provide a driving current todrive red and infrared light emitting diodes used in conjunction withsensors 395 to gather physiological data. The DAU 210 also includes anoptical signal amplifier that includes digitally programmablepotentiometers 321 and a means to convert and amplify outputs from aphotodiode 322. According to an embodiment, the sensors 395 can beincluded in the sensor strip 220.

The DAU 210 can include a memory 324 for data storage. In an embodiment,the memory 324 can comprise a removable Multimedia Memory or SecureDigital card or other types of removable persistent memory. In anotherembodiment, the memory 324 can comprise a fixed flash chip. According toan embodiment, a data transfer interface 325 is provided. According toan embodiment, the data transfer interface comprises a USB data transferchip. In another embodiment, USB transfer capabilities can beincorporated into micro-controller 315.

According to an embodiment, firmware is stored in a memory 317associated with micro-controller 315. According to an embodiment, thememory 317 is a flash memory. According to some embodiments, thefirmware 317 can be updated via data transfer interface 325.Furthermore, according to some embodiments, the memory 317 and thememory 324 can be part of the same persistent memory.

In an embodiment, the firmware is configured to routinely sample andsave signal data received by the DAU 210. According to an embodiment,filtering routines can be used to detect poor quality signal data and tonotify the user via an audible signal generated using audio speaker 316or via a piezo-electric buzzer. For example, if the user has misalignedthe position of the sensor strip 220 on the forehead, the signalsreceived from the sensor strip may of poor quality. The DAU 210 cangenerate an audible alarm or vibrate if the sensor strip needs to berealigned.

In a preferred embodiment the airflow signal is monitored when it isacquired, either by flow amplitude or flow volume to ensure the devicehas been properly applied and being worn correctly. In one embodiment,thresholds are applied to the mean and standard deviation of the airflowamplitude across overlapping time windows (i.e., 5 minutes) to identifyreductions associated with an interface leak. However, in otherembodiments, other measures of low airflow can be performed, includingbut not limited to tidal volume, root mean squared or integratedmeasures, or derivations from band pass filters. According to anembodiment, the EEG/EOG and ECG signals are monitored for high inputimpedance, 60 Hz noise, or spikes in the signal to identify when signalquality is poor. In an embodiment, the signals obtained with areflectance sensor are filtered and monitored to ensure the reflectancesensor is properly positioned for acquiring oximetry.

In one embodiment, DAU 210 can include a wireless transmitter/receiver377 for receiving data from peripheral sensors (i.e., wireless ECGsensors, finger pulse oximeter, respiratory effort bands, sensorsmeasuring leg movements, etc.) and/or transmit signals to an externalcomputer system 390 for real time monitoring of the data being acquiredby the DAU 210. Data acquired from these sensors can be used todetermine the user's sleep architecture and/or to identify sleepdisruptions that can negatively impact sleep quality. In someembodiments, the wireless transmitter/receiver 377 can be integratedinto data transfer module 326 of DAU 210.

According to an embodiment, micro-controller 315 can be based on an ARM32-bit reduced instruction set computer (RISC) instruction set orequivalent architecture. Firmware can be configured to minimize thepower requirements of the ARM chip when the DAU is being used inrecording mode. The computational capacity of the ARM chip can providethe option for firmware to transform the signals during acquisition orprior to data download. For example, fast-Fourier transforms can beapplied to a 512 samples/second EEG signal can quantify the highfrequency power spectral densities of the EEG or EMG without requiringthe large data files to be transferred off line to make thiscomputation. Once high resolution power spectra are computed the EEG canbe saved at 64 samples/second for purposes of visual inspection. Giventhe preference to obtain high fidelity sound signals, in someembodiments it would be beneficial the two-kilohertz signal can bepre-processed and down sampled to reduce data transfer time withoutcompromising analytical power. This approach to down-samplingsignificantly reducing the size of and time to transfer data files fromthe DAU 210 to an external computer system 390 for analysis. Inalternative embodiments, a lower-powered micro-controller is used whenthe DAU is used as a recorder. The micro-controller and also includefeatures such as a temperature monitor, analog to digital converter,and/or the capability to transfer the data file in USB format to reducethe need for extra components.

FIG. 13 illustrates an exemplary computer system that can be used inconjunction the DAU 210 according to an embodiment. In some embodiments,the external computer system 390 is a user's home computer system. Inother embodiments, the external computer system 390 is a doctor'scomputer system. For example, a doctor wishing to perform a sleepassessment on a patient can issue a DAU 210 to the patient. The patientcan then use the DAU 210 at home to capture sleep related data andreturn the DAU 210 to the physician who can then download the data fromthe DAU 210 in order to assess the sleep quality of the patient.

External computer system 390 can include an I/O interface forcommunicating with the data transfer module 326 of DAU 210. According tosome embodiments, the external computer system 390 can interface withthe DAU 210 using either a wired or a wireless interface. In anembodiment, the DAU 210 can download information to the externalcomputer system 390 for analysis, receive firmware updates from externalcomputer system 390, and/or receive configuration data for configuringthe operation of the DAU 210. External computer system 390 includes aprocessor 390 for executing computer-software instructions, and a memory320 can be used to store executable software program modules that can beexecuted by the processor 1310 of external computer system 390.

According to an embodiment, DAU 210 can be configured to perform variousprocessing on the data collected from the sensors and to download theprocessed data to external computer system 390. According to someembodiments, the DAU 210 can capture and store data from the varioussensors and the data is downloaded to external computer system 390 forprocessing. As described above, the DAU can include firmware thatperforms at least a portion of the processing of the signal datacollected before the data is downloaded to the external computer system390.

According to an embodiment, the external computer system 390 can includea data processing and visualization module 1395 that can be used to viewdata collected and/or analyzed by DAU 210 and/or perform analysis andprocessing on the data. According to an embodiment, the externalcomputer system 390 can also include a reporting module 1398 forgenerating reports based on the data collected by the DAU 210.

According an embodiment, the external computer system 390 can include apatient data store 1332, a reporting data store 1334, a diseasemanagement recommendations data store 1336, and a comparative data datastore 1338. In an embodiment, the data stores can be relationaldatabases or other types of persistent and searchable data stores inmemory 1320 of computer system 1320. According to some embodiments, oneor more of the data stores can be stored on an external server and canbe accessed by external computer system 390 via a network connection.

The patient data store 1332 stores patient related data, e.g. a patientidentifier and/or patient demographic information. Patient data from thepatient data store 1332 can be used in the various assessments describedhere for assessing the sleep quality of the user. The reporting datastore 1334 can be used to store reports generated by the reportgeneration module 1398 and can also include report templates that can beused to determine the format of the report and/or the types of analysisto be included in the reports. The disease management recommendationsdata store 1336 can be used to store various treatment recommendationsthat can be included in patient reports based on the analysis of thedata gathered by the DAU 210. The comparative data data store 1338 canbe used to store comparative data from healthy patients and/or patientswith a chronic illness that causes sleep quality to degrade. Thecomparative patient data can be used, in part, to assess the sleepquality of a patient by providing a baseline of healthy and ill patientsagainst which a user's data can be compared.

According to an embodiment, external computer system 390 can be a user'shome computer system, and the DAU 210 can include software fordownloading data captured by the DAU 210 and/or the sensors interfacedwith the DAU 210 to a remote computer system 1350 via a network 1340.For example, in an embodiment, the DAU 210 can include software thatperiodically connects to external computer system 390 via a wirelessinterface, downloads data from the DAU 210 to the external computersystem 390, and triggers a transfer of the data from the externalcomputer system 390 to a remote computer system, such as a doctor'scomputer system or a web portal. In an embodiment, the remote computersystem can be a web portal comprising one more remote servers that cancollect and analyze data received from DAU units. For example, a doctortreating a patient can create an account on the web portal for thatpatient and associate the account with a particular DAU 210. The patentcan then use the DAU 210 to capture data

FIG. 4 illustrates how DAU 210 can be integrated with one or morewireless sensors for measuring various physiological data that can beused to identify sleep disruptions. For example, sensor 410 compriseswireless sensors used to measure pulse/oximetry from the finger, sensor420 comprises a device that obtains electro-cardiographic signals (e.g.,holter monitor), sensor 430 comprises respiratory effort belt, andsensor 440 comprises transducer to measure limb movements. FIG. 4illustrates one possible combination of sensors that can be used.However, in other embodiments other types of sensors for measuringphysiological data can be used and different combinations of sensors canbe used. The data from these sensors can be used to collected data usedby the DAU 210 in the concurrent measurement of signal data related tosleep architecture and of signal data related to sleep disruptions.

As described above, DAU 210 can include a wireless transmitter/receiver377 incorporated into the data transfer module 326 to receive data fromperipheral sensors (i.e., wireless ECG sensors, finger pulse oximeter,respiratory effort bands, sensors measuring leg movements, etc.) and/ortransmit signals to an external computer system 390 for real timemonitoring of the data being acquired by the DAU 210. Data acquired fromthese sensors can be used to determine the user's sleep architectureand/or to identify sleep disruptions that can negatively impact sleepquality.

According to an embodiment, each of these wireless sensor sub-systemscan have a separate power supply and data storage. The DAU 210 and thewireless sensor sub-systems can be integrated to align the data from thesensor sub-systems with the data generated by the DAU 210. For example,the data can be aligned by using a common time stamp on all data thatcan be used to determine when data was recorded by the DAU 210 and/orthe sensor sub-systems. According to an embodiment, this integration canbe achieved by configuring the DAU 210 or one of the sensor sub-systemsto operate to serve as a master device that wirelessly transmits a timestamp that is received by the other integrated components of the system.Each of the components of the system can include a wireless receiver forreceiving the timestamp information and be configured to use thetimestamp information transmitted by the master device to synchronize aninternal clock to that of the master device or to use the timestampinformation transmitted from the master device to timestamp datagenerated by the receiving device. According to an alternativeembodiment, the sensor sub-systems can be integrated with the DAU 210 bycoupling the DAU 210 to the sensor sub-systems using a wire. In such awired configuration, the DAU 210 and the sensor sub-systems can operateusing a common power supply and use common data storage.

In an embodiment, central sympathetic arousals or variability insympathetic activation can be measured with two dry electrodes (i.e.,capable of acquiring the ECG signal through clothes). One benefit ofrecording ECG is to more accurately identify cardiac problems (e.g.,cardiac dysrhythmia, etc.). Alternatively, sympathetic arousals can bedetected with a pulse signal or peripheral arterial tone signal. Thepulse signal can be obtained using a sensor located at the user'sforehead or any other location (e.g., ear, finger, etc.) which obtainscapillary blood flow and is appropriate for either reflectance ortransmittance methodologies/technology.

According to an embodiment, electro-neuro-cardio-respiratory sensorsused to assess sleep quality can be incorporated into a single strip.FIG. 5 is an illustration of the integrated sensor strip 220 of FIGS. 2Aand 2B used to acquire physiological signals according to an embodiment.As described above, the sensor strip 220 can be removeably coupled tothe DAU 210 via a socket connection on the DAU 210 that electricallycouples traces included in the sensor strip 220 with the DAU 210.

FIG. 5 is an illustration of an integrated sensor strip that can be usedto acquire physiological signals that can be used in the concurrentmeasurements related to sleep architecture and sleep disruptions that isperformed by the DAU 210 according to an embodiment. The integratedsensor strip can be used to implement sensor strip 220 associated withDAU 210 in FIG. 2. The sensor strip includes traces 510 that createelectrical circuit connections while holding the sensors against theuser's forehead. Within the sensor strip, EEG/EOG/EMG electrodes 520 areoptimally positioned to measure rapid eye movements, cortical arousals,sleep spindles, K-complexes and stage sleep. One skilled in the art willrecognize that sensors placed on the forehead are capable of acquiringboth the brain's electrical activity and eye movements. In oneembodiment, the strip provides for at least one sensor to be placed offthe forehead in a non-frontal region of the brain to improve thedetection of alpha waves which are used to assess sleep onset andcortical arousals. According to an embodiment, the sensor strip alsoprovides the electrical pathway to drive the red and infrared lightemitting diodes and photodiodes in the reflectance sensor 530. Thereflectance sensor 530 can be used to generate signals for thecalculation of oxyhemoglobin saturation and pulse rate of the user. Fromthe reflectance sensor 530 inputs, a photoplethesmographic signal can bederived to measure respiratory effort via changes in forehead venouspressure.

In a preferred embodiment, the number of sensors included in the sensorstrip is minimized and the connection between the sensors in the sensorstrip and the DAU 210 is a wireless connection. As a result, the sensorstrip can be configured for use on numerous sites, using various sensorcombinations, and can be used with user's having different head sizes.In an embodiment, additional EEG sensors or connectors can be added tothe strip to create the flexible interface to the electronic circuitry.

Furthermore, in some embodiments, inter-electrode spacing can beadjusted to accommodate adolescent and child head sizes. In someembodiments, sensor strap 230 can be integrated into or affixed over thesensor strip 220 to increase ease of preparation. Rather than usingindividual EEG sensors 520 and comfort strip 540, the sensor stripcomprises a sheet of adhesive foam in which the sensors are embedded andwith conductive gel placed over the conductive sensors. The use of foamor alternative potting method ensures the light from the reflectancesensor is transmitted into the skin and not directly to the photodiode.

In a preferred embodiment the sensor strip 220 is detachable from theDAU 210. FIGS. 6A and 6B illustrate views of an enclosure of the DAU 210and the interface with sensor strip 220 according to an embodiment. TheDAU enclosure 600 includes a removable back cover 610 with a securingpush tab 620 that holds the sensor strip 220 in place during use.Removal of the back cover exposes the micro-USB connector 640 and heatdissipating vent holes 650 which allow for data transfer and batteryrecharging. According to an embodiment, the connectors that allow thedevice to be connected to an external power source, such as AC powerfrom the mains power, are not accessible when the system being worn by auser. In an embodiment, electrical pathways between the sensors and theelectronics can be interfaced with one touch-proof connector for the ECGleads 660 and a connector in the center of the enclosure 670 for thesensor strip.

FIG. 2A illustrates a preferred method to measure airflow is by using apneumotachometer mask 240 rather than the nasal cannula 250 that can beused in some embodiments (illustrated in FIG. 2B). FIGS. 7A, 7B, and 7Cillustrate views of a disposable nasal mask 240 capable of measuringminute changes in airflow for use with DAU 210 according to anembodiment. According to an embodiment, the pneumotachometer mask 240can be coupled to the DAU 210 and the pneumotachometer mask 240 cangenerate a signal that represents air flow through the user's nose. TheDAU 210 can receive the signal and collect airflow data.

To accurately quantify airflow, a pneumotachometer-like interface 710provides an air tight seal over and around the nose. A compromised sealcan result in airflow leaking out the sides of the mask, therebycompromising the amplitude and accuracy of the measured airflow volume.Furthermore, too much pressure applied to a conforming material cancause discomfort and create hot spots which restrict blood flow and/orcause pressure sores. In an embodiment, to optimize the comfort andseal, a preferred embodiment includes an interface 730 made frominjection molded silicone with thin dual wall cushion design thatreadily conforms to the patient's facial profile with minimal pressure.According to an embodiment, the interface 730 comprises thin wallsaround the edges of the mask that collapse in an anterior rather thanposterior manner to conform to different facial characteristics. Thisapproach allows less expensive injection molding techniques to be usedto manufacture the disposable nasal mask.

In another embodiment, different thicknesses of material stiffen theregion around where the tubes affix and bridging up to the nose so as toapply downward pressure against the outer walls to optimize the seal. Inan embodiment, spacers 740 ensure the mask remains centered around thenares during use. In an embodiment, the ends of the tubes are positionedinside of the mask to optimize signal amplitude. In alternativeembodiments, the mask is made with a single-wall cushion and from anymaterial which provides the necessary seal and comfort. One skilled inthe art will recognize that nasal masks of different sizes can be usedto accommodate different head and nose shapes and sizes. In anembodiment, if a user is a predominant mouth breather during sleep, afull face interface can be used to quantify nasal plus oral airflow.

According to an embodiment, a differential between the pressure withinthe mask and outside of the mask is needed for the mask to act as apneumotachometer. In a preferred embodiment, this differential pressureis created by holes 720 which impact tidal volume during inspiratory andexpiratory breathing. In an embodiment, tubing is affixed to theinterface at the differential pressure chamber 750 and interfaces withthe DAU 210 which contains a nasal pressure transducer so airflow can bemeasured.

In an embodiment, the number and size of the holes is carefullycontrolled optimize the amplitude of the signal. This tuning shouldavoid any noticeable restriction in inspiratory and expiratory airflow.In the preferred embodiment, a single configuration as to the size andnumber of holes in the opening is utilized for all adults. Inalternative embodiments, a means to change the pressure can be providedby any number of means that increases or decreases the volume of airflowthrough the opening, including but not limited to changing the size andnumber of holes, providing means to partially or fully block any numberof the holes, and/or adding or removing a mesh screen behind theexisting opening. According to some embodiments, a screen can be affixedto either within or on the outside of the mask to increase signalamplitude because the size of the holes that can be manufactured withinjection molding techniques is limited. Furthermore, in alternativeembodiments, different masks can be manufactured for use with the systemthat have appropriate size/volume of openings determined by thepatient's lung volume. Lung volume can be readily estimated based on thesurface area of the patient's body using any number of publishedformulas that include but are not limited to height, weight and age.

According to an embodiment, the mask can incorporate a smaller openingin the pressure cover for masks to be used for children. The masks canbe sized for the smaller head size of a child and adapted for thereduced airflow volume per minute of breathing of a child (in comparisonto an average adult). In a preferred embodiment, the required featuresincorporated into a single use disposable mask minimize dead space inthe mask to reduce the risk of rebreathing of expired CO2. According toan embodiment, stabilizing features are incorporated inside the mask toreduce dead space to ensure the interface does not shift completelyagainst the nares and restrict airflow.

The mask can be secured to the face of the user using varioustechniques. In some embodiments, a slip-tube over nasal cannula tubingand/or at least one self-adjusting strap can be used to secure the maskto the user's face. According to alternative embodiments, a differentnumber of straps can be used and/or integration of the security of thesystem with alternative device(s). For example, more sophisticated meansto easily increase or decrease the tension on the strap using a ladderlock, buckle or looping mechanism can be used. In one embodiment, asingle strap circumvents the neck below the ears, while a second strapstabilizes the interface by affixing it to a device which includes apressure transducer, which itself has a strap to secure it to the head.In an alternative embodiment, that can be used during laboratorypolysomnography, a second strap circumvents the head above the ears. Inanother embodiment, a strap transverses the midline of the head andconnects to a strap(s) which circumvents the head. In yet anotherembodiment, medical grade adhesive is used to tape the mask edges to theface.

Returning now to FIG. 1, FIG. 1 is a flow diagram of the method forassessing sleep quality according to an embodiment. The method can beused to assess the severity and impact of poor sleep quality. FIG. 1 canbe implemented using the various systems described above in FIGS. 1-7.

The method begins with the acquisition of physiological signals from anadult or child at the DAU 210 (step 100). As described above, the dataacquisition system performs concurrent measurements of two categories ofsignal data: (1) signal data related to sleep states, and (2) signaldata related to the type of sleep disruption. In an embodiment, theapparatus used to acquire the physiological signals ideally useselectrodes and sensors, such as sensor strip 220, that can beself-applied with limited skin or scalp preparation, and which monitorssignal quality during use and provides user feedback when signal qualityproblems are detected.

Once the physiological signals are obtained from the sensors, thesignals are analyzed to assess the sleep stage of the user (step 110).As described above, according to some embodiments, the signal data fromthe sensors can be analyzed by the firmware included on DAU 210. Inother embodiments, the data can be downloaded to external computersystem 390 for processing and analysis. According to some embodiments,the DAU 210 can perform pre-processing on the data before the data isdownloaded to the external computer system 390. According to anembodiment, the physiological signals acquired by the DAU 210 can bedownloaded to external computer system 390 and stored in the patientdata store 1332.

In an embodiment, various automated algorithms can be applied to thecaptured signal data. For example, in a preferred embodiment, the EEGsignals are subjected to a filter bank that decomposes the signals intothe frequency bands commonly used in the EEG analyses: eyemovements/artifacts (<1 Hz), delta (1-3 Hz), theta (4-7 Hz), alpha (8-12Hz), sigma (12-16 Hz), beta(18-30 Hz), EMG/artifacts (>32 Hz). Thoseskilled in the art will recognize that any other frequency band can alsobe used where advantageous. Those skilled in the art will also recognizethat the filter bank can be realized with FIR filters, IIR filters,wavelets, or any other similar technique for time-frequencydecomposition of signals.

In a preferred embodiment, REM sleep can be distinguished from non-REMsleep on the basis of ratios between beta EEG power (e.g., 18 to 32 Hz)and delta power (e.g., 1 to 3 Hz) within a pre-defined time window, oron the basis of a measure of agreement between the 2 EEG signalsacquired simultaneously. The measures of agreement, when calculated overa short time window (e.g. 2-5 seconds) will behave markedly differentlyin case of eye movements than in case of delta waves (which can easilybe confused with each other if only frequency analyses are used).According other embodiments, any statistical measure of agreement, suchas Pearson's correlation coefficient or coherence, can be used for thispurpose. Ratios of delta (e.g., 1 to 3.5 Hz) to beta (18-32 Hz) andtheta (4-7 Hz) power are used to identify slow wave sleep.

In alternative embodiments, the detection of sleep stages can beperformed using more sophisticated linear or non-linear mathematicalmodels (e.g., discriminant function, neural network, etc.) withvariables that can be obtained from the EEG, EOG and ECG signals. Shortduration fast-frequency EEG bursts are measured using one-secondmeasures of power spectra to detect sleep spindles (that only appearduring Stage 2 sleep) and EEG arousals (that appear in Stage 1 sleep).The distinction between the spindles and arousals can be made on thebasis of their duration (spindles are shorter, arousals longer than 3seconds). One skilled in the art will recognize that in addition to thetechniques mentioned above, ratios of the power in various frequencybands, or linear combinations (weighted sums) of the power in variousfrequency bands can be used for separation of sleep states andwaveforms. In addition to power spectra analysis of the EEG, one skilledin the art will recognize that variability in the ECG signal increasesduring rapid eye movement sleep. These patterns are different from therapid bradycardia-tachycardia changes that occur as a result of anarousal or with the sinus arrhythmia that can be seen in children. In anembodiment, full-disclosure recording are optionally presented to allowthe signals and automated sleep staging to be manually viewed and editedusing a user interface provided by the data processing and visualizationmodule of the external computer system 390. Standard sleep architectureparameters are then computed, including total sleep, REM and SWS times,sleep, REM and SWS latency, and sleep efficiency. Mean power spectraanalysis computed across stage N1, N2, N3 (SWS) and REM states in thedelta, theta and alpha ranges can be used to identify abnormalcharacteristics associated with abnormal sleep characteristics.

Once the signals have been analyzed to assess the sleep stage, thesignals can be analyzed to identify patterns of sleep and breathingdiscontinuity (step 120). As described above, the data acquisitionsystem collects and records data related to sleep disturbancesconcurrently with data related to sleep architecture. The sleepdisturbance data is analyzed to identify patterns of sleep and breathingdiscontinuity. As described above, according to some embodiments, thesignal data from the sensors can be analyzed by the firmware included onDAU 210. In other embodiments, the data can be downloaded to externalcomputer system 390 for processing and analysis. According to someembodiments, the DAU 210 can also perform some pre-processing on thedata before the data is downloaded to the external computer system 390for analysis.

Sleep medicine practice parameters define standards for the assessmentof sleep and breathing discontinuity. An apnea is defined as a 10-secondcessation in airflow. A hypopnea requires a change in airflow with anaccompanying cortical arousal, or oxyhemoglobin desaturation. Accordingto an embodiment, event scoring can be performed by marking breathingdiscontinuity events in a record and then tallying the events anddividing by total sleep time. In a preferred embodiment, moresophisticated algorithms are applied to the acquired signals to detectand quantify sleep disordered breathing in ways that are not possiblewith visual scoring. Various algorithms that can be used in embodimentsare described below. These algorithms expand upon the rudimentaryapproaches used for visual scoring and can provide improved measures ofsleep disruption and disordered breathing severity that will contributeto improved differential diagnoses or estimated risk for chronicdiseases.

Changes in tidal volume can occur as a result of ventilation or centralbreathing instability vs. airway obstruction. The sleep stage or medicalconditions which contribute to these different types of breathing isimportant because selecting the appropriate therapy is dependentdistinguishing these differences. Flow limitation only occurs when theairway is partially obstructed and manifests as a flattening in theinspiration peak of the airflow signal. An example of flow limitation isprovided in FIG. 7. Thus the presence of at least one flow limitedbreath during a hypopnea is used to confirm that an abnormal breathingevent has an obstructive element. When a tidal volume change is detectedin the absence of flow limitation, the event is characterized as acentral or mixed event.

In a preferred embodiment, flow limitation is detected by comparing theshape of the airflow using two reference airflow shapes, with one shaperepresenting a breath with no obstruction and the other shape reflectinga flow limited breath. There are a number of techniques which can beused to compare signal shapes, including, but not limited to, neuralnetwork and cross correlation analyses, etc. Alternative embodiments canuse as few as one or multiple reference signals for the detection offlow limitation. The accuracy of flow limitation/obstructive breathingdetection can be improved when patterns across previous and subsequentbreaths are compared and combined with the recognition of significantchanges in tidal volume (i.e., apneas). The detection of flow limitationcan be improved when signals are normalized to periods of persistentloud snoring. In an embodiment, the inability to recognize an airflowsignal in at least two breaths is an entry point for differentiatingapnea from a hypopnea. The pattern of distinct inspiratory peaks coupledwith a decrescendo/crescendo change in tidal volume and absence of flowlimitation is used to characterize central events.

Flow limitation can persist for long extended periods of sleepindependent of discrete apneas and hypopneas and thus its prevalencethroughout the night is quantified. In an embodiment, two minute periodsof flow limitation are classified as an equivalent to an apnea orhypopnea events in the calculation of a sleep disorderedbreathing/obstructive index. This obstructive index has been shown toincrease the correlation between abnormal nocturnal breathing anddaytime impairment (i.e., slower reaction times). In an embodiment, thepercentage of the night with flow limitation, stratified by positionand/or snoring level can also be measured and reported to improve thequantification of sleep disordered breathing severity.

When the pneumotachomter nasal mask 240 is properly worn, the amplitudeof the acquired airflow signal is directly proportional to the change inairflow volume during sleep. In other words, this system can provide amore accurate measure of subtle changes in airway obstruction than anyconventional approach. This improvement provides a measurement of eachbreath in units which apply consistently across individuals and allowfor a quantified measure of obstructive breathing (e.g., hypopneaassociated with 20 milliliters per second changes in airflow). Thisimproved precision allows for the assessment of flow limitation toextend beyond a temporal measure (i.e., appearance based on the flowshape) to assess the relationship between tidal volume and flowlimitation. The inventors argue that reductions in tidal volume orminute ventilation resulting from partial or full obstruction provides amore sensitive correlate with the symptoms associated with sleepdisordered breathing (e.g., drowsiness, hypertension, etc.).

In a preferred embodiment an acoustic microphone can be used to acquirequantitative snoring (e.g., measured in decibels) in order to obtain themost accurate information related to the snoring patterns or changes insnoring patterns associated with OSA and/or treatment outcomes. Thesesnoring pattern changes are used as behavioral arousal indicators toindependently confirm that changes in airflow are a result of sleepdisordered breathing (see FIG. 8). During apnea events, snoring willstop as a result of the absence of breathing. During hypopnea events,snoring amplitudes increase as a result of the partial collapse of theairway. Loud steady snoring commonly coincides with periods of flowlimitation. Crescendo and decrescendo patterns at the apex of changingairflow pattern are used to confirm patterns associated with centralsleep apnea. When a patient is not a loud snorer, the behavioralindication of an arousal will be the appearance of a short snore at thetermination of a hypopnea.

FIG. 8 illustrates example data captured by the sensors and the DAU 210and downloaded to a computer system. According to an embodiment, thisdata can be displayed using a user interface generated by the dataprocessing and visualization module of external computer system 390. Inthe embodiment, the user interface illustrates the level of detail ofinformation that can be captured using the system and how the data canbe displayed. In the data illustrated in FIG. 8, the user changedposition from a lateral left position to a supine position. This changeof position results in a drop in flow volume of 50%, constant flowlimitation, and a steady increase in snoring. The movements that occurimmediately after the position change resulted in a number of epochsbeing identified as “awake.” However, only three of the detected eventsare accepted in the calculation of the severity of sleep disorderedbreathing.

In an embodiment, acquiring the snoring/breathing signal with anacoustic microphone at a minimum of two kilohertz allows this signal tobe analyzed in the frequency domain to detect and quantify environmentalnoise or a snoring bed partner. In an embodiment, temporal and frequencyanalysis of this high fidelity signal using wavelets or similar typeanalysis allows snoring to be differentiated from coughing or wheezing.In one embodiment, because snoring represents the most subtle form ofsleep disordered breathing and is considered by some to be anindependent risk factor for carotid artery atherosclerosis, the highfidelity signal is combined with the detection of flow limitation toimprove on the prediction of the onset of this medical condition. Oneskilled in the art recognizes the opportunities to combine voice qualitypattern recognition with changes in actigraphy, airflow, and oximetry tofurther differentiate abnormal respiration. Detection of coughing,wheezing and similar respiratory breathing patterns are useful inproviding a differential diagnosis between OSA and asthma or chronicobstructive pulmonary disease or a lack of response or use of steroidinhalers.

According to an embodiment, to improve detection accuracy of obstructivebreathing related snoring changes, the signal is optimally transformedusing integration or other signal processing routines to accentuate andconsolidate the sound change. As an alternative to an acousticmicrophone, snoring can be detected from any number of means, includingmeasurement of vibration changes, or detection of high-frequency (>70Hz) bursts in the airflow signal. In a preferred embodiment, signalsfrom the microphone are aligned with the breathing from the airflowsignal to ensure noise due to movement or the environment is notcharacterized as snoring. When the snoring signal isconsolidated/transformed in this manner described previously, thesnoring signal must be phase shifted to account for the snoringamplitude extending beyond the negative value associated with theexpiration in the airflow signal. When snoring is analyzed in thismanner, its presence can be used as a behavioral measure of sleep (inthe absence of neuro-physiological measures).

From the actigraphy, long duration or high intensity head or bodymovements are used to identify periods likely contaminated with signalartifact (see FIGS. 9 and 10). The duration and frequency of head orbody movements are also useful in differentiating behavioral sleep fromwake. FIG. 9 includes example data that displays periods withsubstantial head movement (HMOV) indicates the patient is awake.According to an embodiment, this data can be displayed using a userinterface generated by the data processing and visualization module ofexternal computer system 390. Actigraphy is so sensitive it picks upsubtle respiratory-related movements. This pattern can be obtained withaccelerometers mounted in the DAU or affixed to clothing or a belt wornin place of conventional respiratory effort belts. FIG. 9 illustrates anexample of identification of periods for that could be incorrectlydetected as being “awake” periods based on the HMOV, but should insteadbe detected as behavioral sleep based on the snoring signals.

FIG. 10 illustrates example data that shows the influence of increasedrespiratory effort/snoring on the head movement signal. According to anembodiment, this data can be displayed using a user interface generatedby the data processing and visualization module of external computersystem 390. FIG. 10 illustrates that some periods could be incorrectlyclassified as “awake” periods based on head movements reflecting anincreased effort to breathe. FIG. 11 shows surrogate measure ofrespiratory effort derived from the X, Y, and Z actigraphy channels(tilt 1, 2, and 3 respectively) compared to respiratory effort obtainedfrom chest wall and abdomen effort belts and an esophageal balloon. FIG.11 includes data that shows an example of respiratory effort obtainedfrom the tri-directional signals obtained with actigraphy (e.g., Tilt 1,Tilt 2, and Tilt 3), compared with to respiratory effort obtained withchest and abdomen belts and esophageal balloon. According to anembodiment, this data can be displayed using a user interface generatedby the data processing and visualization module of external computersystem 390. In a preferred embodiment, the actigraphy signal is filteredto isolate signals associated with breathing and respiratory effort toremove these from the head movement signal. Simple filtering techniquestimed to the frequency of the breathing is sufficient to isolate theeffort signal as well as remove it from the head movement signal.Alternatively, adaptive filtering is employed to reduce harmonics causedby applying the incorrect band-pass filters. The resulting movementmeasure shows only gross/important changes. The resulting surrogaterespiratory effort signal (when combined with the airflow signal)provides for improved differentiation between obstructive and centralbreathing events.

In an embodiment, the actigraphy signal can be used to detect periodiclimb or body movements as shown in FIG. 12. According to an embodiment,this data can be displayed using a user interface generated by the dataprocessing and visualization module of external computer system 390.Periodic limb movements appear in the actigraphic signals acquired onthe head as movements of short duration and high intensity that occur atregular intervals with no apneas or hypopneas in the airflow signal. Inone embodiment, accelerometers could be placed on the ankles as aseparate adjunct device that directly measures leg movements andtransmits this information wirelessly to the main device. Becausearousal from either sleep disordered breathing or periodic limbmovements contribute to sleep fragmentation and daytime somnolence, thefrequency, intensity and duration of head movements during time in bedcan be used to independently quantify the quality of sleep. Thesemeasures of sleep quality can be especially useful for use in diagnosingsleep quality children, because cortical arousals are more difficult tomeasure and disrupted sleep causes daytime drowsiness, a symptom thataffects those with childhood OSA and attention deficit/hyperactivitydisorders.

As described previously, the actigraphy signals can be usedindependently to distinguish sleep from wake, and/or combined withchanges in snoring and/or airflow to further improve the accuracy ofsleep/wake detection. FIG. 9 highlights temporal periods of snoringbetween periods of gross head movement, with snoring indicating thepatient is asleep and the head movement indicating the patient is awake.In the preferred embodiment, sleep and wake are determined independentlyfor movement and snoring, and algorithms are applied to improve theaccurate detection of behavioral sleep and wake when the results fromthe independent measures conflict. These rules require the accuratedetection of snoring and timing of snoring with airflow to ensure thatenvironmental noise is not contributing to the misclassification ofsleep. Returning now to FIG. 1, once behavioral sleep/wake isdetermined, epoch classifications can be compared to classificationspreviously made by neurophysiological means (see step 110 of FIG. 1) toimprove the overall accuracy of the sleep/wake classifications.

Arousals which disrupt sleep continuity can be distinguished from anynumber of signal patterns and preferably multiple signals are used toconfirm the veracity of the arousal. For example, sleep spindles andarousals have a similar frequency domain characteristic, and it may bedifficult to distinguish spindles from short arousals. Sleep spindlesindicate the patient is in N2 sleep and the absence of behavioralarousals would be expected. Cortical arousals are expected to beaccompanied by changes in heart rate, head movement, respiration, bloodflow or any number of physiological markers. The temporal alignment ofarousals from multiple sources can be performed within an arousal windowthat accommodates physiological phase delays (e.g., circulation delaycauses four-second latency in pulse rate). The measurement ofnon-abating sympathetic hyperactivity and its impact on autonomicnervous system dysfunction and sleep continuity/quality can be achievedwith a frequency domain analysis of the heart rate variability (usingeither ECG or pulse rate).

Once the signals have been analyzed patterns of sleep and breathingdiscontinuity, the sleep architecture for the user, as well as therespiration and fragmentation patterns for the user can be summarized(step 130). As described above, according to some embodiments, thesignal data from the sensors can be analyzed by the firmware included onDAU 210. In other embodiments, the data can be downloaded to externalcomputer system 390 for processing and analysis. According to someembodiments, the DAU 210 can perform pre-processing on the data beforethe data is downloaded to the external computer system 390.

In an embodiment, the number of events per hour derived from each of theneuro-cardio-respiratory measures are measured separately andsubsequently combined to profile the level of sleep disruption or sleepcontinuity. The frequency and duration of menopausal hot flashes canalso be measured and temporally associated with sleep stage and arousal.In a preferred embodiment, hot flash related sympathetic arousals aremeasured directly by electrocardiogram, pulse oximetry, or peripheralarterial tone, however, indirect measures such as the electricalconductivity of the skin (galvanic skin reaction, “GSR”) can bealternatively used. In the preferred embodiment, the sleep studies areconducted over multiple nights with a report providing night-to-nightcomparisons of each of the measures described previously to betterprofile variability in sleep architecture and continuity.

A number of biological markers can also be optionally obtained toincrease diagnostic accuracy, the predicted likelihood of disease onset,or to guide treatment (step 140). The biological marker results can bederived from any number of sample means including blood, saliva, serum,urine, hair, etc. that are sensitive to a particular disease phenotype.For example, the result from a glucose tolerance test can be combinedwith the amount of slow wave sleep to assess the risk of Type IIdiabetes. Similarly test results for inflammatory markers such asC-reactive protein assays, Interleukin-6 (IL-6) or otherpro-inflammatory cytokine can be similarly combined with sleep measures,e.g., amount of slow wave sleep, total sleep time, amount of REM sleep,or sleep continuity disrupted by repeated sympathetic arousals, todetermine the likelihood of an individual having or developingcardiovascular disease, hypertension, chronic fatigue syndrome,fibromyalgia, depression, and autoimmune disorders metabolic X syndrome.

In a preferred embodiment, the information obtained from the detectionof sleep quality is combined with questionnaire responses to improvediagnostic capabilities of the system (step 150). The questionnaires canidentify subjective measures of insomnia, depression, mood, anxiety,sleepiness and/or other factors that can impact sleep quality. Accordingto some embodiments, the questionnaires can be implemented on a webportal and the user is presented with a web page or series of web pagesthat present the questionnaire to the user and capture the user'sresponse. According to an alternative embodiment, the questionnaires canbe implemented using client-server software on the external computersystem 390 that presents the user with a user interface that displaysthe questionnaire, captures the user's responses, and stores theresponses on a memory devices such as a SD card and/or transmits theresponses to a doctor or clinician for use in analyzing the sleepquality of the patient using the DAU. According to an embodiment, thequestionnaire responses can be stored in the patient data store 1332.The questionnaire responses can then be correlated with detection ofsleep quality described above to identify factors that may be negativelyimpacting sleep quality.

For example, if a patient had a high pre-test probability of having OSAbut little to no sleep disordered breathing was detected, a report caninclude a suggestion that the patient be referred to an expert for aconsultation. A system which includes information derived fromanalytical techniques that utilize nocturnal measures of sleepdisordered breathing in combination with clinical, history and physicalinformation is especially useful in children. Because there is asignificant overlap in the symptoms related to attentiondeficit/hyperactivity disorders (ADHD), obstructive sleep apnea (OSA)and asthma, the capability of a clinician to obtain an accuratedifferential diagnosis is improved if the probabilities of a patienthaving each of the diseases is provided. In an embodiment, the studyreport can incorporate information derived from questionnaire responsesthat combine demographic (i.e., age, gender, etc.), anthropomorphic(i.e., height, weight, body mass index, neck, stomach or hipcircumference, etc.), history of diseases, and abnormal self-reportedlevels of depression, mood, anxiety, and sleepiness. These responses canbe obtained from any number of conventional questionnaire instruments(e.g., Epworth Sleepiness Score, Beck Depression Index, State-TraitAnxiety Index, Pittsburgh Insomnia Rating Scale, Profile of Mood states,etc.) or any combination of questions that provide the appropriateinformation. In a preferred embodiment a limited number of questionnaireresponses can be selected and combined with the sleep quality measuresto develop a patient profile.

Abnormal psychophysiological conditions are then identified in someembodiments (step 160). In an embodiment, sleep quality is associatedwith its impact on daytime psychophysiological states or conditions,e.g., somnolence, mood and memory. This association can be obtained fromany number of conventional neuropsychological assessments, including butnot limited to computerized behavioral tests, continuous performancetests or psychomotor vigilance tests, neurocognitive factors change orsimilar means with or without the acquisition of physiological signals(i.e., EEG, ECG, etc.). Conversely, systems designed for objectiveassessment of daytime psychophysiological states or conditions willinclude sleep architecture and continuity measures derived from thedescribed system.

To improve the diagnostic accuracy of the sleep study results, theindividual's data can be additionally compared to a database of valuesfrom healthy individuals and/or patients with chronic diseases (step170). According to an embodiment, the database of values from healthyindividuals and/or individuals with chronic diseases can be stored incomparative data data store 1338, and the data from the comparative datadata store 1338 can be compared to the individual user's data byexecuting comparison algorithms on external computer system 390. Thismethodology can be applied to the data to calculate odds ratios orprobability estimates as to the likelihood an asymptomatic individualmay be developing a chronic disease that can negatively impact sleepquality (e.g., Type II diabetes, hypertension, etc.). Questionnaireresponses obtained can be used to establish a pre-test probability of anindividual need for a sleep study. Alternatively, the results obtainedprior and subsequent to a treatment intervention can allow the change inrisk level based on the treatment efficacy to be determined andreported. One skilled in the art will recognize that any number ofstatistical procedures are appropriate for assigning a pretestprobability or odds ratio including but not limited to logisticalregression analysis, etc.

A study results report can then be generated that integrates thefindings from the steps described above with disease managementrecommendations (step 180). According to an embodiment, the diseasemanagement recommendations can be stored in disease managementrecommendations data store 1336 and the external computer system 390 canexecute a comparison algorithm to identify which disease recommendationsare appropriate for the for the individual user for whom the studyresults report is generated. In an embodiment, the disease managementrecommendations can be selected based on the results of the analysis ofthe signal data obtained by the DAU 210 and other physiological andpsychopyshiological data obtained using one or more of the techniquesdescribed above. For example, if the user has been identified to havehypertension, recommendations for treating hypertension can be providedin the sleep study report. These recommendations can be used by the userand/or a treating physician to manage conditions that can have anegative impact on sleep quality.

In an embodiment, the study results report can be stored in thereporting data store 1334. According to an embodiment, the externalcomputer system includes a user interface module for displaying thereport data. In some embodiments, where the external computer system isimplemented as a web portal, a web page or series of web pages can beprovided that allow patients and/or clinicians to generate and viewreports.

The sleep study metrics obtained from this system can provide the basisfor report recommendations that are used to assist clinicians andpatients in the management of chronic diseases. For insomnia patients,the sleep assessment study allows one to determine the severity of thesleep disturbances and identify insomnia attributed to physical causeswhich result in repeated sympathetic arousals (e.g., hot flashes,periodic limb movements, etc.). Alternatively, when insomnia appears tobe caused by psychological problems (i.e., acute stress, depression,anxiety) it should be addressed with relaxation techniques, changes indiet and exercise, improved sleep hygiene (i.e., regular sleep routine)and counseling to help resolve the psychological problems. If hypnotics,anxiolytics, or other medications are prescribed, a repeat sleep studyis recommended to assess changes in sleep architecture resulting fromuse or discontinued use of the medication(s).

The biomarker of early onset REM sleep and increased REM density andduration can be used to objectively confirm major depression,independent of clinical impressions or self-reported mood depressionscores. These markers can also predict successful treatment outcomes anddifferentiate/identify those who have mood disorders related toinsomnia. A repeat sleep study after prescription of Selective SerotoninReuptake Inhibitors (SSRI) medications can assess treatment outcomes bydetermining if there has been changed or normalized consolidation andreduction in REM density and duration. For a patient with menopausalsymptoms, the relationship between the frequency and duration of hotflashes, sleep stage and sleep continuity and the temporal associationof sympathetic arousals and hot flashes can be objectively determined.If it is determined that the duration of REM sleep increases thesympathetic arousal and results in periodic sleep state insomnia, thentreatments which reduce REM sleep can be recommended. Repeated measuretesting is appropriate for the treatment of any medical condition thatincludes the need to reduce the latency or amount of REM sleep.Treatment to reduce REM sleep is not limited to pharmacological means,and includes feedback or other methods that shift sleep stage from REMto non-REM sleep (e.g., auto or vibro-tactile feedback). Repeat testingcan also confirm that appropriate or normal amounts of SWS are obtainedto reduce the risk of obesity and insulin resistance or to assess thebenefit non-hormonal treatment interventions.

Sleep architecture and continuity study results can be combined with ablood glucose tolerance assay are used to fully diagnose borderline TypeII diabetes cases and recommend early management and monitoring.Measures of SWS and delta activity across sleep stages will be used todetermine if abnormal sleep architecture or patterns are contributing toinsulin resistance. If the results indicate that the patient has a sleepdisorder recommendations for an appropriate study can be made. If sleepdisorder is found, then treatment for the disorder can be recommendedbefore, or in conjunction with treatment or preventative measures takenfor the diabetes. If risk of Type II diabetes is indicated, thenbehavioral intervention to improve sleep hygiene and duration can beadvised in addition to regular exercise and other preventative measures.If diabetes has developed and no indication of a concurrent sleepdisorder is indicated, then sleep studies can be recommended or used tomonitor treatment and to ensure that a sleep disorder (such as OSA) doesnot develop.

Disturbed sleep with abnormal sympathetic arousals in conjunction withimmune markers (e.g., C-reactive protein assays, Interleukin-6 (IL-6) orother pro-inflammatory cytokine measures) can be used to determine earlyrisk for development of cardiovascular disease or confirm inflammationassociated with fibromyalgia, chronic fatigue, or similar diseases.

Measures of total sleep time, the amount of SWS, and sleep fragmentationindependently or in combination with measures of between leptin andghrelin can be used to assess the potential for sleep qualitycontributing to the consumption of high carbohydrate foods in obesepatients. Repeated testing provides objective assessment of improvementsin total sleep time and SWS as part of a dietary and exerciseintervention.

In a preferred embodiment, the novel measures obtained from the airflowand actigraphy signals and described previously can be incorporated intothe report to improve its clinical and diagnostic value. For example,the breathing frequency during sleep can be averaged and compared tonormative values to identify breathing abnormalities. Quantifying thevariability of the non-flow limited breathing amplitude and snoring andrelating this variability by sleep position can provide uniqueinformation related to subtle disease states. The frequency, intensityand duration of head movements during time in bed can be used toindependently quantify the quality of sleep which is especially relevantbecause children with OSA, ADHD and disturbed sleep from other causesall have daytime drowsiness. Thus, reporting sleep quality can be usedto differentiate children who have disturbed sleep from other causesfrom those with sleep disordered breathing. Quantification techniquesfor the described measures include linear, Gaussian based approaches aswell as non-linear mathematical applications applied to the entire studyor segments of the study.

In a preferred embodiment this system is deployed using a web-basedportal, such as the remote server described above. This approacheliminates the problems associated with operating desk top softwarewithin a local area network designed to control confidential patientinformation. The web-portal is designed to upload recording from the DAUand questionnaire responses prior to applying the analytical software.The recordings and questionnaire responses can be uploaded independentlyand/or to the portal via a mobile device, electronic medical recordssystem or from a desk top computer. The web-portal provides cliniciansthe means to host inspection of the full disclosure recording and/orreview and download of the study report.

One skilled in the art will recognize that the described system can beconfigured for multi-modality approaches to assess sleep quality. Table1 provides a plethora of exemplary system combinations which aredependent on the needs of the user (e.g., ease of use, size, powerrequirements, cost, physiological information needed) to diagnose adisorder/disease or assess treatment outcomes as a result of anintervention. Option 1, for example, provides the simplest configurationto assess changes in sleep disordered breathing as a result of atreatment intervention such as an oral or Provent™ appliance. Options 2through 5 provide greater precision as to the measurement of respiratoryrelated arousals on sleep quality. Options 6 and 7 are equivalent to andcould be used as a replacement for the current method used to obtainlaboratory polysomnography. If a clinician suspects the patient hasdepression but hasn't ruled out OSA, then options 8 through 10 canprovide the needed information. If OSA has been ruled out as a possiblediagnoses, then option 11 is the simplest approach to assess changes insleep architecture and sleep continuity as a result of interventions formenopausal hot flashes or depression-like REM patterns, etc. Options 12through 14 can provide more thorough assessments of physiology impactedby poor sleep quality.

According to an embodiment, where oximetry is not required, the size ofthe DAU 210 allows the DAU to be readily mounted on the user's forehead.When the DAU is located on the forehead it can be more difficult toacquire more than one channel of EEG/EOG needed to accurately assesssleep architecture. When options 1 through 5 are implemented with theDAU above the forehead, the system can be used to assess treatmentoutcomes when worn simultaneously with continuous positive airwaypressure (CPAP). In an embodiment, the pressure/flow signal derived fromthe CPAP tube can be directly input to the DAU 210 so long as the nasalpressure transducer can measure +/−10 inches/H20. In an alternativeembodiment, a pitot tube can be placed in-line between the CPAP tube andmask to extract the airflow signal for input into the high resolutionDAU nasal pressure transducer (i.e., +/−1 inch/H20).

One skilled in the art will recognize that the options provided beloware non-inclusive of all possible combinations which can be achievedwith the system described above. Given the flexible deployment of thesensor/signal sets, steps 140 through 180 in FIG. 1 remain part of thesystem, it simply affects the amount of information that can be obtainedwhen compared to a database of data with similar measures.

TABLE 1 Sensor, Signal and Analyses Combinations to Quantify SleepQuality Airflow- Behavioral- Cardiac- Acoustic- Limb mask, Arousals,Sleep architecture, Oxygen patterns, breathing, Respiratory MovementLocation cannula sleep/wake arousals saturation arousals snoring effortdetection of DAU 1 Yes Yes Yes Forehead 2 Yes Yes Yes Forehead 3 Yes YesYes Yes Forehead 4 Yes Yes Yes Yes Forehead 5 Yes Yes Yes Yes Forehead 6Yes Yes Yes Yes Yes Yes Yes Head 7 Yes Yes Yes Yes Yes Yes Yes Yes Head8 Yes Yes Yes Head 9 Yes Yes Yes Head 10 Yes Yes Yes Yes Yes Head 11 YesYes Head 12 Yes Yes Yes Head 13 Yes Yes Yes Yes Head 14 Yes Yes Yes YesYes Head

Those of skill in the art will appreciate that the various illustrativemodules and method steps described in connection with the abovedescribed figures and the embodiments disclosed herein can often beimplemented as electronic hardware, software, firmware or combinationsof the foregoing. To clearly illustrate this interchangeability ofhardware and software, various illustrative modules and method stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. Skilled persons can implement the describedfunctionality in varying ways for each particular application, but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the invention. In addition, the grouping offunctions within a module or step is for ease of description. Specificfunctions can be moved from one module or step to another withoutdeparting from the invention.

Moreover, the various illustrative modules and method steps described inconnection with the embodiments disclosed herein can be implemented orperformed with hardware such as a general purpose processor, a digitalsignal processor (“DSP”), an application specific integrated circuit(“ASIC”), field programmable gate array (“FPGA”) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor is hardware and can be amicroprocessor, but in the alternative, the processor can be anyhardware processor or controller, microcontroller. A processor can alsobe implemented as a combination of computing devices, for example, acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration.

Additionally, the steps of a method or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in computer orcontroller accessible on computer-readable storage media including RAMmemory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form ofstorage medium including a network storage medium. An exemplary storagemedium can be coupled to the processor such the processor can readinformation from, and write information to, the storage medium. In thealternative, the storage medium can be integral to the processor. Theprocessor and the storage medium can also reside in an ASIC.

The above description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the invention. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles described herein can beapplied to other embodiments without departing from the spirit or scopeof the invention. Thus, it is to be understood that the description anddrawings presented herein represent exemplary embodiments of theinvention and are therefore representative of the subject matter whichis broadly contemplated by the present invention. It is furtherunderstood that the scope of the present invention fully encompassesother embodiments and that the scope of the present invention isaccordingly limited by nothing other than the appended claims.

1. A system for assessing sleep quality of a user, the systemcomprising: a sensor strip comprising at least one sensor configured todetect data indicative of sleep disturbances of a user, the sensor stripbeing positioned on the user's forehead; and a wearable data acquisitionunit coupled to the sensor strip; the data acquisition unit beingconfigured to receive and collect data from the sensors in the sensorstrip.
 2. The system of claim 1 wherein the sensor strip also includesat least one sensor for detecting data indicative of a sleep state ofthe user.
 3. The system of claim 1 further comprising: a nasalpneumotachometer coupled to the data acquisition unit to provide thedata acquisition unit with a signal indicative of airflow through theuser's nose, the pneumotachometer being capable of detecting minutechanges in airflow through the user's nose.
 4. The system of claim 1further comprising: a nasal cannula coupled to the data acquisition unitto provide the data acquisition unit with a signal indicative of airflowthrough the user's nose by detecting a difference in pressure betweenthe nasal cavity and the atmosphere.
 5. The system of claim 1 furthercomprising: at least one wireless sensor placed on the user's body forcollecting data indicative of sleep disturbances of the user, the atleast one wireless sensor being located outside of the sensor strip. 6.The system of claim 5 at least one wireless sensor is configured tocollect data indicative of obstructive sleep apnea.
 7. The system ofclaim 1 wherein data indicative of the frequency and duration of thesleep disturbances is collected by the data acquisition unit for use inidentify underlying medical conditions of the user.
 8. The system ofclaim 1 wherein the data acquisition unit is configured to download thecollected sensor data to an external computer system, and wherein theexternal computer system is configured to analyze the data tocharacterize types of sleep disturbance events experienced by the userfor use in diagnosing underlying medical conditions of the user.
 9. Asystem for assessing sleep quality of a user, the system comprising: awearable data acquisition unit for concurrently measuring dataindicative of a sleep state of a user and indicative of sleepdisturbances experienced by the user; a sensor strip coupled to the dataacquisition unit, the sensor strip comprising at least one sensorconfigured to detect a psychophysiological data of the user and toprovide the data to the data acquisition unit, wherein the dataacquisition unit is configured to receive and collect data from thesensors in the sensor strip; and a nasal pneumotachometer coupled to thedata acquisition unit to provide the data acquisition unit with ameasurement of airflow through the user's nose.
 10. The system of claim9 wherein the sensor strip is disposable and can be decoupled from thedata acquisition unit and replaced with a new sensor strip.
 11. Thesystem of claim 10 wherein the sensor strip includes an adhesive backingfor removeably affixing the sensor strip to the user's body.
 12. Thesystem of claim 9 wherein the sensor strip comprises a sheet of adhesivefoam in which the at least one sensor is embedded and wherein the sensorstrip includes conductive gel placed over each conductive sensor. 13.The system of claim 9 wherein the data acquisition unit is held inposition on the user's head using a removable headband.
 14. The systemof claim 9 further comprising: at least one wireless sensor placed onthe user's body for collecting data indicative of sleep disturbances ofthe user, the at least one wireless sensor being located outside of thesensor strip.
 15. The system of claim 9 wherein the data acquisitionunit includes a sensor driving unit that is configured to provide adriving current to one or more sensors included in the sensor strip. 16.The system of claim 9 wherein the sensor strip includes at least oneelectrode for measuring EEG, EOG, or EMG and at least one reflectancesensor.
 17. The system of claim 9 wherein the sensor strip includestraces that create electrical circuit connections while holding the atleast one sensor against the user's forehead.
 18. A system for assessingsleep quality of a user, the system comprising: a sensor stripcomprising at least two sensors configured to detect data indicative ofsleep disturbances of a user, the sensors being configured to collectdata of a different type, the sensor strip being removeably affixed tothe user's body; and a wearable data acquisition unit coupled to thesensor strip; the data acquisition unit being configured to receive andcollect data from the sensors in the sensor strip.
 19. The system ofclaim 18 wherein the sensor strip is disposable and can be decoupledfrom the data acquisition unit and replaced with a new sensor strip. 20.The system of claim 19 wherein the sensor strip includes an adhesivebacking for removeably affixing the sensor strip to the user's body. 21.A method for assessing sleep quality of a user using a wearable dataacquisition unit, the method comprising: acquiring physiological signaldata at the data acquisition device from a sensor strip affixed to theuser's forehead while the user is sleeping; downloading the signal datafrom the data acquisition device to an external computer system;analyzing the physiological signal data to assess a sleep stage of theuser using the external computer system; analyzing the physiologicalsignal data for patterns of sleep and breathing discontinuity using theexternal computer system; and identifying abnormal biological markersbased on samples obtained from the user to predict the likelihood ofonset of disease using the external computer system; summarizing sleeparchitecture, respiration, and fragmentation patterns from the signaldata using the external computer system; and generating a sleep studyreport from the signal data using the external computer system.
 22. Themethod of claim 21, further comprising: receiving subjectivequestionnaire responses for at least one questionnaire related tofactors that can impact sleep quality; and correlating the subjectivequestionnaire responses with the signal data received by the dataacquisition unit using the external computer system.
 23. The method ofclaim 21, further comprising: receiving psychophysiological assessmentdata for the users; and analyzing the psychophysiological assessmentdata to identify abnormal psychophysiological conditions using theexternal computer system.
 24. The method of claim 21, furthercomprising: comparing the signal data to a database of comparativesubject data, wherein the database includes data for healthy and forchronic diseased patients, to determine the likelihood that the user maybe developing a chronic disease that can negatively impacting sleepquality.
 25. The method of claim 21, further comprising: identifyingdisease management recommendations for the user by correlating thesignal data and physiological and psychophysiological data related tothe user with disease management recommendations.